mirror of https://github.com/langgenius/dify.git
fix qc
This commit is contained in:
takatost
parent
a05fcedd61
commit
d71eae8f93
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@ -15,12 +15,13 @@ from core.model_runtime.entities.model_entities import ModelType
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from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
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from core.model_runtime.utils.encoders import jsonable_encoder
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from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
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from core.prompt.entities.advanced_prompt_entities import MemoryConfig
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from core.prompt.utils.prompt_message_util import PromptMessageUtil
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from core.workflow.entities.base_node_data_entities import BaseNodeData
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from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeRunResult, NodeType, SystemVariable
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from core.workflow.entities.variable_pool import VariablePool
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from core.workflow.nodes.base_node import BaseNode
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from core.workflow.nodes.llm.entities import LLMNodeData
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from core.workflow.nodes.llm.entities import LLMNodeData, ModelConfig
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from extensions.ext_database import db
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from models.model import Conversation
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from models.provider import Provider, ProviderType
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@ -64,10 +65,10 @@ class LLMNode(BaseNode):
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node_inputs['#context#'] = context
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# fetch model config
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model_instance, model_config = self._fetch_model_config(node_data)
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model_instance, model_config = self._fetch_model_config(node_data.model)
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# fetch memory
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memory = self._fetch_memory(node_data, variable_pool, model_instance)
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memory = self._fetch_memory(node_data.memory, variable_pool, model_instance)
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# fetch prompt messages
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prompt_messages, stop = self._fetch_prompt_messages(
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@ -89,7 +90,7 @@ class LLMNode(BaseNode):
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# handle invoke result
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result_text, usage = self._invoke_llm(
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node_data=node_data,
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node_data_model=node_data.model,
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model_instance=model_instance,
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prompt_messages=prompt_messages,
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stop=stop
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@ -119,13 +120,13 @@ class LLMNode(BaseNode):
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}
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)
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def _invoke_llm(self, node_data: LLMNodeData,
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def _invoke_llm(self, node_data_model: ModelConfig,
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model_instance: ModelInstance,
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prompt_messages: list[PromptMessage],
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stop: list[str]) -> tuple[str, LLMUsage]:
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"""
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Invoke large language model
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:param node_data: node data
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:param node_data_model: node data model
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:param model_instance: model instance
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:param prompt_messages: prompt messages
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:param stop: stop
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@ -135,7 +136,7 @@ class LLMNode(BaseNode):
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invoke_result = model_instance.invoke_llm(
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prompt_messages=prompt_messages,
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model_parameters=node_data.model.completion_params,
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model_parameters=node_data_model.completion_params,
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stop=stop,
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stream=True,
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user=self.user_id,
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@ -286,14 +287,14 @@ class LLMNode(BaseNode):
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return None
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def _fetch_model_config(self, node_data: LLMNodeData) -> tuple[ModelInstance, ModelConfigWithCredentialsEntity]:
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def _fetch_model_config(self, node_data_model: ModelConfig) -> tuple[ModelInstance, ModelConfigWithCredentialsEntity]:
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"""
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Fetch model config
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:param node_data: node data
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:param node_data_model: node data model
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:return:
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"""
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model_name = node_data.model.name
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provider_name = node_data.model.provider
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model_name = node_data_model.name
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provider_name = node_data_model.provider
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model_manager = ModelManager()
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model_instance = model_manager.get_model_instance(
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@ -326,14 +327,14 @@ class LLMNode(BaseNode):
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raise QuotaExceededError(f"Model provider {provider_name} quota exceeded.")
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# model config
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completion_params = node_data.model.completion_params
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completion_params = node_data_model.completion_params
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stop = []
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if 'stop' in completion_params:
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stop = completion_params['stop']
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del completion_params['stop']
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# get model mode
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model_mode = node_data.model.mode
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model_mode = node_data_model.mode
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if not model_mode:
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raise ValueError("LLM mode is required.")
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@ -356,26 +357,25 @@ class LLMNode(BaseNode):
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stop=stop,
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)
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def _fetch_memory(self, node_data: LLMNodeData,
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def _fetch_memory(self, node_data_memory: Optional[MemoryConfig],
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variable_pool: VariablePool,
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model_instance: ModelInstance) -> Optional[TokenBufferMemory]:
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"""
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Fetch memory
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:param node_data: node data
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:param node_data_memory: node data memory
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:param variable_pool: variable pool
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:return:
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"""
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if not node_data.memory:
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if not node_data_memory:
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return None
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# get conversation id
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conversation_id = variable_pool.get_variable_value(['sys', SystemVariable.CONVERSATION])
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conversation_id = variable_pool.get_variable_value(['sys', SystemVariable.CONVERSATION.value])
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if conversation_id is None:
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return None
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# get conversation
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conversation = db.session.query(Conversation).filter(
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Conversation.tenant_id == self.tenant_id,
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Conversation.app_id == self.app_id,
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Conversation.id == conversation_id
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).first()
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@ -2,6 +2,7 @@ from typing import Any, Optional
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from pydantic import BaseModel
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from core.prompt.entities.advanced_prompt_entities import MemoryConfig
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from core.workflow.entities.base_node_data_entities import BaseNodeData
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@ -23,21 +24,6 @@ class ClassConfig(BaseModel):
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name: str
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class WindowConfig(BaseModel):
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"""
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Window Config.
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"""
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enabled: bool
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size: int
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class MemoryConfig(BaseModel):
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"""
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Memory Config.
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"""
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window: WindowConfig
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class QuestionClassifierNodeData(BaseNodeData):
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"""
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Knowledge retrieval Node Data.
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@ -1,25 +1,17 @@
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import json
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from collections.abc import Generator
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from typing import Optional, Union, cast
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from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
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from core.entities.model_entities import ModelStatus
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from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
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from core.memory.token_buffer_memory import TokenBufferMemory
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from core.model_manager import ModelInstance, ModelManager
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from core.model_runtime.entities.llm_entities import LLMUsage
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from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageRole
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from core.model_runtime.entities.model_entities import ModelType
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from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
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from core.model_runtime.utils.encoders import jsonable_encoder
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from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
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from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate
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from core.prompt.simple_prompt_transform import ModelMode
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from core.prompt.utils.prompt_message_util import PromptMessageUtil
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from core.workflow.entities.base_node_data_entities import BaseNodeData
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from core.workflow.entities.node_entities import NodeRunResult, NodeType, SystemVariable
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from core.workflow.entities.node_entities import NodeRunResult, NodeType
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from core.workflow.entities.variable_pool import VariablePool
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from core.workflow.nodes.base_node import BaseNode
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from core.workflow.nodes.llm.llm_node import LLMNode
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from core.workflow.nodes.question_classifier.entities import QuestionClassifierNodeData
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from core.workflow.nodes.question_classifier.template_prompts import (
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@ -31,28 +23,28 @@ from core.workflow.nodes.question_classifier.template_prompts import (
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QUESTION_CLASSIFIER_USER_PROMPT_2,
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QUESTION_CLASSIFIER_USER_PROMPT_3,
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)
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from extensions.ext_database import db
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from models.model import Conversation
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from models.workflow import WorkflowNodeExecutionStatus
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class QuestionClassifierNode(BaseNode):
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class QuestionClassifierNode(LLMNode):
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_node_data_cls = QuestionClassifierNodeData
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_node_type = NodeType.QUESTION_CLASSIFIER
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def _run(self, variable_pool: VariablePool) -> NodeRunResult:
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node_data: QuestionClassifierNodeData = cast(self._node_data_cls, self.node_data)
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node_data = cast(QuestionClassifierNodeData, node_data)
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# extract variables
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query = variable_pool.get_variable_value(variable_selector=node_data.query_variable_selector)
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variables = {
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'query': query
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}
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# fetch model config
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model_instance, model_config = self._fetch_model_config(node_data)
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model_instance, model_config = self._fetch_model_config(node_data.model)
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# fetch memory
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memory = self._fetch_memory(node_data, variable_pool, model_instance)
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memory = self._fetch_memory(node_data.memory, variable_pool, model_instance)
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# fetch prompt messages
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prompt_messages, stop = self._fetch_prompt_messages(
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prompt_messages, stop = self._fetch_prompt(
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node_data=node_data,
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context='',
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query=query,
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@ -62,7 +54,7 @@ class QuestionClassifierNode(BaseNode):
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# handle invoke result
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result_text, usage = self._invoke_llm(
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node_data=node_data,
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node_data_model=node_data.model,
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model_instance=model_instance,
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prompt_messages=prompt_messages,
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stop=stop
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@ -117,126 +109,20 @@ class QuestionClassifierNode(BaseNode):
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return {
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"type": "question-classifier",
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"config": {
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"instructions": "" # TODO
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"instructions": ""
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}
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}
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def _fetch_model_config(self, node_data: QuestionClassifierNodeData) \
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-> tuple[ModelInstance, ModelConfigWithCredentialsEntity]:
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"""
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Fetch model config
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:param node_data: node data
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:return:
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"""
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model_name = node_data.model.name
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provider_name = node_data.model.provider
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model_manager = ModelManager()
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model_instance = model_manager.get_model_instance(
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tenant_id=self.tenant_id,
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model_type=ModelType.LLM,
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provider=provider_name,
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model=model_name
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)
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provider_model_bundle = model_instance.provider_model_bundle
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model_type_instance = model_instance.model_type_instance
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model_type_instance = cast(LargeLanguageModel, model_type_instance)
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model_credentials = model_instance.credentials
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# check model
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provider_model = provider_model_bundle.configuration.get_provider_model(
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model=model_name,
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model_type=ModelType.LLM
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)
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if provider_model is None:
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raise ValueError(f"Model {model_name} not exist.")
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if provider_model.status == ModelStatus.NO_CONFIGURE:
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raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
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elif provider_model.status == ModelStatus.NO_PERMISSION:
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raise ModelCurrentlyNotSupportError(f"Dify Hosted OpenAI {model_name} currently not support.")
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elif provider_model.status == ModelStatus.QUOTA_EXCEEDED:
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raise QuotaExceededError(f"Model provider {provider_name} quota exceeded.")
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# model config
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completion_params = node_data.model.completion_params
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stop = []
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if 'stop' in completion_params:
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stop = completion_params['stop']
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del completion_params['stop']
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# get model mode
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model_mode = node_data.model.mode
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if not model_mode:
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raise ValueError("LLM mode is required.")
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model_schema = model_type_instance.get_model_schema(
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model_name,
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model_credentials
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)
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if not model_schema:
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raise ValueError(f"Model {model_name} not exist.")
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return model_instance, ModelConfigWithCredentialsEntity(
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provider=provider_name,
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model=model_name,
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model_schema=model_schema,
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mode=model_mode,
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provider_model_bundle=provider_model_bundle,
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credentials=model_credentials,
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parameters=completion_params,
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stop=stop,
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)
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def _fetch_memory(self, node_data: QuestionClassifierNodeData,
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variable_pool: VariablePool,
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model_instance: ModelInstance) -> Optional[TokenBufferMemory]:
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"""
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Fetch memory
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:param node_data: node data
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:param variable_pool: variable pool
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:return:
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"""
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if not node_data.memory:
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return None
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# get conversation id
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conversation_id = variable_pool.get_variable_value(['sys', SystemVariable.CONVERSATION])
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if conversation_id is None:
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return None
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# get conversation
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conversation = db.session.query(Conversation).filter(
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Conversation.tenant_id == self.tenant_id,
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Conversation.app_id == self.app_id,
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Conversation.id == conversation_id
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).first()
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if not conversation:
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return None
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memory = TokenBufferMemory(
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conversation=conversation,
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model_instance=model_instance
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)
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return memory
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def _fetch_prompt_messages(self, node_data: QuestionClassifierNodeData,
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query: str,
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context: Optional[str],
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memory: Optional[TokenBufferMemory],
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model_config: ModelConfigWithCredentialsEntity) \
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def _fetch_prompt(self, node_data: QuestionClassifierNodeData,
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query: str,
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context: Optional[str],
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memory: Optional[TokenBufferMemory],
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model_config: ModelConfigWithCredentialsEntity) \
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-> tuple[list[PromptMessage], Optional[list[str]]]:
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"""
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Fetch prompt messages
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Fetch prompt
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:param node_data: node data
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:param inputs: inputs
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:param files: files
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:param query: inputs
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:param context: context
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:param memory: memory
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:param model_config: model config
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@ -310,63 +196,3 @@ class QuestionClassifierNode(BaseNode):
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return prompt_messages
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else:
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raise ValueError(f"Model mode {model_mode} not support.")
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def _invoke_llm(self, node_data: QuestionClassifierNodeData,
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model_instance: ModelInstance,
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prompt_messages: list[PromptMessage],
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stop: list[str]) -> tuple[str, LLMUsage]:
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"""
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Invoke large language model
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:param node_data: node data
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:param model_instance: model instance
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:param prompt_messages: prompt messages
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:param stop: stop
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:return:
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"""
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invoke_result = model_instance.invoke_llm(
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prompt_messages=prompt_messages,
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model_parameters=node_data.model.completion_params,
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stop=stop,
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stream=True,
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user=self.user_id,
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)
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# handle invoke result
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text, usage = self._handle_invoke_result(
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invoke_result=invoke_result
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)
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# deduct quota
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LLMNode.deduct_llm_quota(tenant_id=self.tenant_id, model_instance=model_instance, usage=usage)
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return text, usage
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def _handle_invoke_result(self, invoke_result: Generator) -> tuple[str, LLMUsage]:
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"""
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Handle invoke result
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:param invoke_result: invoke result
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:return:
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"""
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model = None
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prompt_messages = []
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full_text = ''
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usage = None
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for result in invoke_result:
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text = result.delta.message.content
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full_text += text
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self.publish_text_chunk(text=text, value_selector=[self.node_id, 'text'])
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if not model:
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model = result.model
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if not prompt_messages:
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prompt_messages = result.prompt_messages
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if not usage and result.delta.usage:
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usage = result.delta.usage
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if not usage:
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usage = LLMUsage.empty_usage()
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return full_text, usage
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